Agent-based models are often described as bottom-up because macro-level phenomena emerge from the micro-level interactions of agents. These macro-level phenomena include fixed points, cycles, dynamic patterns, and long transients. In this paper, I explore the link between micro-level characteristics-learning rules, diversity, network structure, and externalities-and the macro-level patterns they produce. I focus on why we need agent-level modeling, on how these models produce emergent phenomenon, and on how agent-based models help understand outcomes of social systems in a way that differs from the analytic, equilibrium approach.